kancollefandomcom-20200213-history
User blog:Homuhomu123/Epilogue for Exp't A~E
While running expeditions for the next experiment, I came up to write down something, as if a short reflection on the pervious 5 experiments. Including myself, we all like to conclude things from brief observations, and generalize things as much as possible. As for the game, studies from players provided great ideas on game mechanics, and most of them just seemed to be consistent with our understanding. I guess that is how we formed our "common sense" for the game Kancolle. Well, it's a good idea to formulate things, believe and follow it simply. But it could be a hard time when contradiction happens and you probably have to change your Kancolle mentality Confirmation bias prevails, and objectivity is not always consistent with making life easier. Back to the experiments. During the last 2 weeks, methodology on data collection greatly improved. Stricter precedures were followed, and larger sample size were used. Various explanations on the data popped out, trying to best fit every data at hand & on the Kancolle test blog. Also, as I mentioned in the postscript of Exp't E, it's very hard to differentiate experiment errors and "modifier errors". For example, if we yielded an inaccurate estimate for BBV base rate, and use it as asumption, how could we precisely evaluate the value of another factor cumulative with this one? The good thing is that, after multiple revisions on the formula, ~70% of my estimates for CI/DA rates are always within 4% of the actual (for #ATK>160), if compared to the test blog & my exp'ts. Efforts were made to minimize deviations, by suspecting previous assumptions & coming up with new, legid hypothesis. It's rather complicated and not so scientific. But if in most situations the final revised formula works well for Artillery Spotting Trigger Rate Estimate, the primary objective of the project will be achieved. >> Next Step: [ Exp't F ] : Evaluation of CA/V Base Rate on Double Attack At last, I'll put down the theoretically recommended sample size for future tests, based on standard error calculation (GREAT thanks to @Mathiaszealot :) ceil( (x*(1 - x) / ( E / Z) ^2) ) where E = error = 4%, and Z = 1.28 ( for 80% confidence ) Ture %. min.trials.rqrd 21 170 22 176 23 182 24 187 25 193 26 198 27 202 28 207 29 211 30 216 31 220 32 223 33 227 34 230 35 233 36 236 37 239 38 242 39 244 40 246 41 248 42 250 43 251 44 253 45 254 46 255 47 256 48 256 49 256 ' 50 256 << used to ' 51 256 52 256 53 256 54 255 55 254 56 253 57 251 58 250 59 248 60 246 61 244 62 242 63 239 64 236 65 233 66 230 67 227 68 223 69 220 70 216 71 211 72 207 73 202 74 198 75 192 76 187 77 182 78 176 79 170 Category:Blog posts